There are 31 new articles in PLoS ONE today. As always, you should rate the articles, post notes and comments and send trackbacks when you blog about the papers. You can now also easily place articles on various social services (CiteULike, Mendeley, Connotea, Stumbleupon, Facebook and Digg) with just one click. Here are my own picks for the week – you go and look for your own favourites:

Mediterranean climate is found on five continents and supports five global biodiversity hotspots. Based on combined downscaled results from 23 atmosphere-ocean general circulation models (AOGCMs) for three emissions scenarios, we determined the projected spatial shifts in the mediterranean climate extent (MCE) over the next century. Although most AOGCMs project a moderate expansion in the global MCE, regional impacts are large and uneven. The median AOGCM simulation output for the three emissions scenarios project the MCE at the end of the 21st century in Chile will range from 129-153% of its current size, while in Australia, it will contract to only 77-49% of its current size losing an area equivalent to over twice the size of Portugal. Only 4% of the land area within the current MCE worldwide is in protected status (compared to a global average of 12% for all biome types), and, depending on the emissions scenario, only 50-60% of these protected areas are likely to be in the future MCE. To exacerbate the climate impact, nearly one third (29-31%) of the land where the MCE is projected to remain stable has already been converted to human use, limiting the size of the potential climate refuges and diminishing the adaptation potential of native biota. High conversion and low protection in projected stable areas make Australia the highest priority region for investment in climate-adaptation strategies to reduce the threat of climate change to the rich biodiversity of the mediterranean biome.

A major unanswered question in the evolution of Homo sapiens is when anatomically modern human populations began to expand: was demographic growth associated with the invention of particular technologies or behavioral innovations by hunter-gatherers in the Late Pleistocene, or with the acquisition of farming in the Neolithic? We investigate the timing of human population expansion by performing a multilocus analysis of≥20 unlinked autosomal noncoding regions, each consisting of ~6 kilobases, resequenced in ~184 individuals from 7 human populations. We test the hypothesis that the autosomal polymorphism data fit a simple two-phase growth model, and when the hypothesis is not rejected, we fit parameters of this model to our data using approximate Bayesian computation. The data from the three surveyed non-African populations (French Basque, Chinese Han, and Melanesians) are inconsistent with the simple growth model, presumably because they reflect more complex demographic histories. In contrast, data from all four sub-Saharan African populations fit the two-phase growth model, and a range of onset times and growth rates is inferred for each population. Interestingly, both hunter-gatherers (San and Biaka) and food-producers (Mandenka and Yorubans) best fit models with population growth beginning in the Late Pleistocene. Moreover, our hunter-gatherer populations show a tendency towards slightly older and stronger growth (~41 thousand years ago, ~13-fold) than our food-producing populations (~31 thousand years ago, ~7-fold). These dates are concurrent with the appearance of the Late Stone Age in Africa, supporting the hypothesis that population growth played a significant role in the evolution of Late Pleistocene human cultures.

This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain.

We conducted a large-scale transcriptomic profiling of selected regions of the central nervous system (CNS) across three species of honey bees, in foragers that were performing dance behavior to communicate to their nestmates the location, direction and profitability of an attractive floral resource. We used microarrays to measure gene expression in bees from Apis mellifera, dorsata and florea, species that share major traits unique to the genus and also show striking differences in biology and dance communication. The goals of this study were to determine the extent of regional specialization in gene expression and to explore the molecular basis of dance communication. This “snapshot” of the honey bee CNS during dance behavior provides strong evidence for both species-consistent and species-specific differences in gene expression. Gene expression profiles in the mushroom bodies consistently showed the biggest differences relative to the other CNS regions. There were strong similarities in gene expression between the central brain and the second thoracic ganglion across all three species; many of the genes were related to metabolism and energy production. We also obtained gene expression differences between CNS regions that varied by species: A. mellifera differed the most, while dorsata and florea tended to be more similar. Species differences in gene expression perhaps mirror known differences in nesting habit, ecology and dance behavior between mellifera, florea and dorsata. Species-specific differences in gene expression in selected CNS regions that relate to synaptic activity and motor control provide particularly attractive candidate genes to explain the differences in dance behavior exhibited by these three honey bee species. Similarities between central brain and thoracic ganglion provide a unique perspective on the potential coupling of these two motor-related regions during dance behavior and perhaps provide a snapshot of the energy intensive process of dance output generation. Mushroom body results reflect known roles for this region in the regulation of learning, memory and rhythmic behavior.

Alarm substances are airborne chemical signals, released by an individual into the environment, which communicate emotional stress between conspecifics. Here we tested whether humans, like other mammals, are able to detect emotional stress in others by chemosensory cues. Sweat samples collected from individuals undergoing an acute emotional stressor, with exercise as a control, were pooled and presented to a separate group of participants (blind to condition) during four experiments. In an fMRI experiment and its replication, we showed that scanned participants showed amygdala activation in response to samples obtained from donors undergoing an emotional, but not physical, stressor. An odor-discrimination experiment suggested the effect was primarily due to emotional, and not odor, differences between the two stimuli. A fourth experiment investigated behavioral effects, demonstrating that stress samples sharpened emotion-perception of ambiguous facial stimuli. Together, our findings suggest human chemosensory signaling of emotional stress, with neurobiological and behavioral effects.

Electronic noses, E-Noses, are instruments designed to reproduce the performance of animal noses or antennae but generally they cannot match the discriminating power of the biological original and have, therefore, been of limited utility. The manner in which odorant space is sampled is a critical factor in the performance of all noses but so far it has been described in detail only for the fly antenna. Here we describe how a set of metal oxide (MOx) E-Nose sensors, which is the most commonly used type, samples odorant space and compare it with what is known about fly odorant receptors (ORs). Compared with a fly’s odorant receptors, MOx sensors from an electronic nose are on average more narrowly tuned but much more highly correlated with each other. A set of insect ORs can therefore sample broader regions of odorant space independently and redundantly than an equivalent number of MOx sensors. The comparison also highlights some important questions about the molecular nature of fly ORs. The comparative approach generates practical learnings that may be taken up by solid-state physicists or engineers in designing new solid-state electronic nose sensors. It also potentially deepens our understanding of the performance of the biological system.

Intense interest surrounds the recent expansion of US National Institutes of Health (NIH) budgets as part of economic stimulus legislation. However, the relationship between NIH funding and cardiovascular disease research is poorly understood, making the likely impact of this policy change unclear. The National Library of Medicine’s PubMed database was searched for articles published from 1996 to 2006, originating from U.S. institutions, and containing the phrases “cardiolog,” “cardiovascular,” or “cardiac,” in the first author’s department. Research methodology, journal of publication, journal impact factor, and receipt of NIH funding were recorded. Differences in means and trends were tested with t-tests and linear regression, respectively, with P≤0.05 for significance. Of 117,643 world cardiovascular articles, 36,684 (31.2%) originated from the U.S., of which 10,293 (28.1%) received NIH funding. The NIH funded 40.1% of U.S. basic science articles, 20.3% of overall clinical trials, 18.1% of randomized-controlled, and 12.2% of multicenter clinical trials. NIH-funded and total articles grew significantly (65 articles/year, P<0.001 and 218 articles/year, P<0.001, respectively). The proportion of articles receiving NIH funding was stable, but grew significantly for basic science and clinical trials (0.87%/year, P<0.001 and 0.67%/year, P = 0.029, respectively). NIH-funded articles had greater journal impact factors than non NIH-funded articles (5.76 vs. 3.71, P<0.001). NIH influence on U.S. cardiovascular research expanded in the past decade, during the period of NIH budget doubling. A substantial fraction of research is now directly funded and thus likely sensitive to budget fluctuations, particularly in basic science research. NIH funding predicts greater journal impact.